80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
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import base64
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import logging
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from openai import OpenAI
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from Config.Config import ALY_LLM_API_KEY, ALY_LLM_MODEL_NAME, ALY_LLM_BASE_URL
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# 获取模块专属日志器
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logger = logging.getLogger(__name__)
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def extract_text_from_image(image_path):
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"""
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从图片中提取文字内容（排除几何图形中的文字）
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Args:
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image_path: 图片文件的绝对路径
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Returns:
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str: 提取的文字内容，若失败则返回空字符串
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"""
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try:
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# 读取图片并转换为base64
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with open(image_path, "rb") as image_file:
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base64_image = base64.b64encode(image_file.read()).decode('utf-8')
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image_url = f"data:image/png;base64,{base64_image}"
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# 初始化OpenAI客户端（QWen QVQ）
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client = OpenAI(
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api_key=ALY_LLM_API_KEY,
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base_url=ALY_LLM_BASE_URL
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)
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# 构建API请求（遵循QWen的messages格式）
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prompt = "【任务】仅提取图像中的文字内容（排除几何图形中的文字），并用【】包裹结果。"
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prompt += "【要求】1. 不添加任何解释、分析或额外说明；2. 不包含思考过程；3. 仅返回提取的文本内容。"
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prompt += "【2】如上面提到的信息外，其它信息一概不要输出！\n"
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completion = client.chat.completions.create(
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model="qvq-max",
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messages=[{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": image_url}},
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{"type": "text", "text": prompt}
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]
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}],
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stream=True
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)
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# 处理流式响应
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extracted_text = []
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for chunk in completion:
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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# 仅保留content字段，过滤reasoning_content
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if hasattr(delta, 'content') and delta.content:
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extracted_text.append(delta.content)
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# 如果有reasoning_content也一并处理（根据QWen模型特性）
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
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extracted_text.append(delta.reasoning_content)
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logger.info("文字提取完成")
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return ''.join(extracted_text)
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except FileNotFoundError:
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logger.error(f"图片文件不存在: {image_path}")
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except Exception as e:
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logger.error(f"文字提取失败: {str(e)}", exc_info=True)
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return ""
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# 示例用法
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if __name__ == "__main__":
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# 配置日志（实际项目中应在主程序统一配置）
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logging.basicConfig(level=logging.INFO)
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# 测试图片路径
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test_image = r"D:\dsWork\dsProject\dsLightRag\Test\extracted\a62dce9d67c818accf94113aabefe172\1_1_TXT.png"
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# 调用提取函数
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text = extract_text_from_image(test_image)
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print("提取结果:\n", text)
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